Nonlinear system identification using least squares support vector machine tuned by an adaptive particle swarm optimization

作者：Wang, Shuen; Han, Zhenzhen; Liu, Fucai; Tang, Yinggan^*

来源：International Journal of Machine Learning and Cybernetics, 2015, 6(6): 981-992.

DOI：10.1007/s13042-015-0403-0

• Nonlinear system
• Identification
• Least squares support vector machine
• Adaptive particle swarm optimization

In this paper, we present a method for nonlinear system identification. The proposed method adopts least squares support vector machine (LSSVM) to approximate a nonlinear autoregressive model with eXogeneous (NARX). First, the orders of NARX model are determined from input-output data via Lipschitz quotient criterion. Then, an LSSVM model is used to approximate the NARX model. To obtain an efficient LSSVM model, a novel particle swarm optimization with adaptive inertia weight is proposed to tune the hyper-parameters of LSSVM. Two experimental results are given to illustrate the effectiveness of the proposed method.

• 出版日期2015-12
• 单位燕山大学; 中国科学院电工研究所